{"id":"W2055338357","doi":"10.1139/f04-162","title":"Tailoring palaeolimnological diatom-based transfer functions","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université Laval","keywords":"Diatom; Pruning; Set (abstract data type); Ecology; Relevance (law); Computer science; Artificial neural network; Variable (mathematics); Artificial intelligence; Environmental science; Machine learning; Mathematics; Biology; Botany","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007094557,0.0009257266,0.0005036545,0.0005777698,0.0003433501,0.0004944724,0.0009379145,0.0005765989,0.0007364479],"category_scores_gemma":[0.002912804,0.0004701808,0.0005356388,0.0003123039,0.0002429017,0.0006183529,0.0005827089,0.0006857096,0.0002892563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005424881,"about_ca_system_score_gemma":0.0007209181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008040057,"about_ca_topic_score_gemma":0.01122048,"domain_scores_codex":[0.9998819,0.00003092572,0.000007031219,0.00003686724,0.00002376919,0.00001955942],"domain_scores_gemma":[0.9995154,0.0002537738,0.00005401295,0.0000547142,0.0001012133,0.00002094227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000261846,0.00004181909,0.00593511,0.00002256351,0.00006440823,0.00003881105,0.0000603531,0.9540825,0.005768528,0.0007124533,0.000146418,0.03310084],"study_design_scores_gemma":[0.000002243578,0.00001052418,0.0005765388,0.00000180235,0.000006760504,0.000007092431,0.000006295571,0.9977044,0.001154109,0.0004023057,0.0001244012,0.000003460404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2777413,0.00008253915,0.7196915,0.00007357627,0.00002143897,0.00005847914,0.0001723393,0.0007479272,0.001410919],"genre_scores_gemma":[0.8247988,0.00007655266,0.1731742,0.00004444485,0.00001663522,0.0001322058,0.0004213638,0.0001424814,0.00119331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008040057,"threshold_uncertainty_score":0.01598656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0368306354774313,"score_gpt":0.2169869015737487,"score_spread":0.1801562660963174,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}